optimization

Casey Muratori's first refterm lecture lays out philosophies of optimization, distinguishing optimization from non-pessimization, which means avoiding needless work. It uses his fast terminal renderer project to show how fast computers really are.

Kaze Emanuar explains how he rewrote and optimized the Super Mario 64 source code so it runs far faster on real N64 hardware. He covers performance bottlenecks and fixes in detail for his ROM hacks.

Matthias Wandel tests old and new code on a range of PCs, including a new mini PC, to measure how much faster modern hardware really is. He also shows optimization techniques such as branchless code, with benchmark programs on GitHub.

Modern Vintage Gamer examines the claim from the Super Mario 64 decompilation project that the game shipped unoptimized, and puts it in the context of 1996 N64 development.

The winning demo from Revision 2015's oldskool competition, running on a real 1981 IBM PC with CGA at 4.77MHz. It uses extreme tricks to produce effects, graphics and sound far beyond what the hardware was thought capable of.

Morphcat Games explains how they fit Micro Mages, a full-featured NES game, into 40 kilobytes. It covers compression of sprites and level layouts and other space-saving tricks.

Wes Bos breaks down the techniques behind the McMaster-Carr website's speed: server-rendered HTML, prefetching, CDN and service worker caching, preloading, critical CSS and fixed-size images. He also notes its old-school jQuery and YUI JavaScript.

A short clip of Casey Muratori from Handmade Hero explaining why he rejects the object-oriented mindset and how to move away from it. It is part rant, part programming philosophy.

A ten-minute clip from Jonathan Blow's talk on preventing the collapse of civilization, arguing that software quality and the knowledge to build it are in decline.

2kliksphilip examines UserBenchmark, a hardware comparison site known for strongly biased, pro-Intel writing. The video explains the site's credibility problems and points viewers to better sources for CPU and GPU information.

Digital Foundry investigates why Resident Evil Village on PC runs worse with Capcom's Denuvo DRM than a cracked version. They benchmark and confirm performance and frame-pacing differences.

3Blue1Brown's first chapter on deep learning explains what neurons and layers are and the math underlying a basic neural network, using handwritten digit recognition with visual animations.

SethBling shows MarI/O, a program using neural networks and the NEAT genetic algorithm to learn to play Super Mario World. The description links the source code, the paper and background reading.

Tom7 explains and demos software that learns to play NES games by finding score patterns in memory and then searching for inputs that raise it. It is the video companion to his SIGBOVIK paper.

The first episode of Google's Machine Learning Recipes series. It introduces machine learning and builds a first supervised-learning classifier in about six lines of Python using scikit-learn.

Sentdex demonstrates GAN Theft Auto, a GameGAN-based neural network that recreates GTA V so the network itself is the playable environment. He shows the results and releases the code.

3Blue1Brown's third deep learning chapter gives a visual, intuitive account of backpropagation and what happens to a neural network as it learns. A follow-up video covers the underlying calculus.

Robert Miles explains reward modelling, in which an AI learns what humans want from their preference comparisons instead of a hand-written reward function. He walks through the 2017 paper on deep reinforcement learning from human preferences.

A short smartphone-recorded clip from a 2014 event in Paris where DeepMind CEO Demis Hassabis presents an AI learning to play old Atari games. The system learns from only the video signal and the score. Audio quality is poor.

Mark Rober and Jabril build an app that uses machine learning to decode baseball sign sequences. The video also explains neural networks in a simple, accessible way.

A short cartoon explaining how language models like ChatGPT work, from autocomplete and conditional probabilities to neural networks, gradient descent and backpropagation. It shows how networks act as universal function approximators.

agadmator walks through a game in which DeepMind's AlphaZero beat Stockfish, ending in a famous zugzwang that leaves the engine helpless. He gives commentary on the moves.

Karl Sims's 1994 demonstration of evolved virtual creatures: block-bodied creatures whose bodies and controllers are evolved through selection, mutation and recombination to swim, walk and compete.

Two Minute Papers covers OpenAI's multi-agent hide-and-seek experiment, in which agents learned emergent tool use and found ways to break the game's rules. It summarizes the 'Emergent Tool Use from Multi-Agent Interaction' paper.

A video presenting an Intel research paper on enhancing the photorealism of synthetic images. A convolutional network uses rendering intermediates, shown on GTA V footage that comes out looking like real street scenes.

Matt Parker of Stand-up Maths demonstrates MENACE, Donald Michie's 1961 machine made of matchboxes that learns to play noughts and crosses through reinforcement. He builds and trains a physical version.

A Two Minute Papers episode on NVIDIA's recurrent denoising autoencoder, which cleans up noisy Monte Carlo path-traced image sequences. It shows how a neural network can produce usable renders from very few samples.

Bob Nystrom shows his process for hand-drawing the diagrams in his book Crafting Interpreters, from pencil sketches to tracing and inking. It is a time-lapse of the whole illustration workflow.

Anders Hejlsberg introduces the native port of the TypeScript compiler, written in Go, which promises roughly 10x faster tooling. He demos compiling VS Code and explains where the speedup comes from.

3Blue1Brown's chapter 4 on deep learning gives a more formal, symbol-based account of backpropagation calculus, connecting the intuition from the previous chapter to the math found in textbooks and code.

First part of a six-part recording of the 1973 Lighthill debate on AI, where Sir James Lighthill's critical report on AI research was contested by figures including McCarthy and Michie.

A demo of PREDATOR (TLD), a real-time object tracker that learns the appearance of its target and improves while it tracks. The technology was later developed commercially.

Microsoft Research's Rick Rashid demonstrates a speech recognition and translation system, built on deep neural networks, that turns his spoken English into Chinese speech in 2012.

A six-minute journey through the layers of a neural network using Deep Dream, with each frame fed back into the network starting from random noise and the targeted layer changing every four seconds. It is set to downtempo music.

3kliksphilip summarizes Valve's GDC talk on VACNet, a deep-learning system that flags CS:GO cheaters and feeds the Overwatch review queue. It covers Trust Factor, VAC, and how the data is collected and analyzed.

A JuliaCon 2016 talk by Shashi Gowda showing an APL implementation in Julia. APL code is translated to Julia and JIT compiled to machine code.

Two Minute Papers covers a paper on speeding up fluid and smoke simulation with convolutional networks. It contrasts the approach with earlier regression forest methods.

A React Conf talk by Xuan Huang previewing React Forget, a compiler that automatically memoizes components. It argues for making memoization the default so developers need not write useMemo or useCallback by hand.

Demis Hassabis of DeepMind speaks at Google Zeitgeist, saying human-level AI is decades away but the debate should start now. He covers DeepMind's focus on game-playing agents.

Andrej Karpathy's Databricks talk on Software 2.0, the shift from human-written code to code produced by neural network training. It covers how developer work moves toward curating datasets, the advantages and challenges, and opportunities for new tooling.